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"Moisture content"
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The Effect of Moisture Content at Compaction and Grain Size Distribution on the Shear Strength of Unsaturated Soils
The soil moisture content at shearing and other factors, including dry density and grain size, influence its shear strength. This study investigated the effect of moisture content at compaction and grain size distribution on the unsaturated soil shear strength. Triaxial compression tests were performed in the laboratory using the modified triaxial apparatus on silica sands No. 3 and 6 without fines and with 20% fines to explore the unsaturated soil shear strength characteristics. Test samples were compacted and sheared at various combinations of the soil’s optimum and residual moisture content. The analysis of the triaxial compression test results shows that moisture content at compaction and the grain size distribution influence the unsaturated soil shear strength. The test samples compacted at optimum moisture content showed higher peak shear strength when sheared at residual moisture content. Further, test results show that the test samples of soil without fines, when compacted at residual moisture content, show higher peak shear strength at optimum moisture content. The finding of this study endorses considering the moisture content at compaction for the geotechnical design of structures while predicting the soil shear strength.
Journal Article
Large increases of multi-year droughts in north-western Europe in a warmer climate
Three consecutive dry summers in western Europe (2018–2019–2020) had widespread negative impacts on society and ecosystems, and started societal debate on (changing) drought vulnerability and adaptation measures. We investigate the occurrence of multi-year droughts in the Rhine basin, with a focus on event probability in the present and in future warmer climates. Additionally, we investigate the temporally compounding physical drivers of multi-year drought events. A combination of multiple reanalysis datasets and multi-model large ensemble climate model simulations was used to provide a robust analysis of the statistics and physical processes of these rare events. We identify two types of multi-year drought events (consecutive meteorological summer droughts and long-duration hydrological droughts), and show that these occur on average about twice in a 30 year period in the present climate, though natural variability is large (zero to five events can occur in a single 30 year period). Projected decreases in summer precipitation and increases in atmospheric evaporative demand, lead to a doubling of event probability at 1
∘
C additional global warming relative to present-day and an increase in the average length of events. Consecutive meteorological summer droughts are forced by two, seemingly independent, summers of lower than normal precipitation and higher than normal evaporative demand. The soil moisture response to this temporally compound meteorological forcing has a clear multi-year imprint, resulting in a relatively larger reduction of soil moisture content in the second year of drought, and potentially more severe drought impacts. Long-duration hydrological droughts start with a severe summer drought followed by lingering meteorologically dry conditions. This limits and slows down the hydrological recovery of soil moisture content, leading to long-lasting drought conditions. This initial exploration provides avenues for further investigation of multi-year drought hazard and vulnerability in the region, which is advised given the projected trends and vulnerability of society and ecosystems.
Journal Article
RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques
Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.
Journal Article
Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century
Projections for near-surface soil moisture content in Europe for the 21st century were derived from simulations performed with 26 CMIP5 global climate models (GCMs). Two Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered. Unlike in previous research in general, projections were calculated separately for all four calendar seasons. To make the moisture contents simulated by the various GCMs commensurate, the moisture data were normalized by the corresponding local maxima found in the output of each individual GCM. A majority of the GCMs proved to perform satisfactorily in simulating the geographical distribution of recent soil moisture in the warm season, the spatial correlation with an satellite-derived estimate varying between 0.4 and 0.8. In southern Europe, long-term mean soil moisture is projected to decline substantially in all seasons. In summer and autumn, pronounced soil drying also afflicts western and central Europe. In northern Europe, drying mainly occurs in spring, in correspondence with an earlier melt of snow and soil frost. The spatial pattern of drying is qualitatively similar for both RCP scenarios, but weaker in magnitude under RCP4.5. In general, those GCMs that simulate the largest decreases in precipitation and increases in temperature and solar radiation tend to produce the most severe soil drying. Concurrently with the reduction of time-mean soil moisture, episodes with an anomalously low soil moisture, occurring once in 10 years in the recent past simulations, become far more common. In southern Europe by the late 21st century under RCP8.5, such events would be experienced about every second year.
Journal Article
Physiological Response of Soybean Plants to Water Deficit
Soybean is an important cash crop in the world, and drought is the main reason for the loss of soybean plants productivity, with drought stress during the most water-sensitive flowering period of soybeans. In this article, drought-tolerant variety Heinong 44 (HN44) and drought-sensitive variety Heinong 65 (HN65) were used as experimental materials. Drought treatment was carried out at the early flowering stage. The method of controlling soil moisture content was used to simulate different degrees of drought, and the physiological changes of these two varieties of soybean under different soil moisture contents were studied. The results showed that with a decrease in soil moisture content, the content of malondialdehyde (MDA) in soybean leaves increased significantly; the activities of peroxidase (POD), catalase (CAT), and ascorbic acid peroxidase (APX) increased first and then decreased; the content of proline, soluble sugar, and soluble protein increased; and the total antioxidant capacity (T-AOC) increased significantly. When the soil moisture content was 15.5%, the degree of membrane lipid peroxidation, osmotic regulatory substances, antioxidant enzyme activity, and T-AOC increased the most, and the decrease in drought-tolerant variety HN44 was significantly less than that of drought-sensitive variety HN65. Our research reveals the response law of soybean crops to physiological characteristics under water deficit and provides theoretical basis and guiding significance for drought-resistant cultivation and breeding of soybean.
Journal Article
Twenty-first century drought analysis across China under climate change
Under global warming, according to results obtained from offline drought indices driven by projections of general circulation models (GCMs), future droughts in China will worsen but the results are not consistent. We analyzed changes in droughts covering the entire hydrologic cycle using outputs of GCMs of the 6th Coupled Model Intercomparison Project (CMIP6) for SSP2-4.5 and SSP5-8.5 climate scenarios, and compared the results with that of popular, offline drought indices [the self-calibrating Palmer Drought Severity Index (scPDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Actual Evapotranspiration Index (SPAEI)]. Among meteorological, agricultural, and hydrological drought indices tested under both SSP scenarios, the results obtained from SPAEI and scPDSI agree better with univariate drought indices than SPEI. scPDSI generally agrees well with agricultural droughts (Standardized Soil Moisture Index with the surface soil moisture content; SSIS). Future droughts estimated using soil moisture analysis are more widespread than that from precipitation and runoff analysis in humid regions of South China by the end of the twenty-first century. In arid northwestern China and Inner Mongolia, drought areas and severity based on scPDSI and SSIS forced with the SSP scenarios show obvious decreasing trends, in contrast to increasing trends projected in South China. Trends projected using SPEI contradict those projected by other drought indices in non-humid regions. Therefore, selecting appropriate drought indices is crucial in project representative future droughts and provides meaningful information needed to achieve effective regional drought mitigation strategies under climate warming impact.
Journal Article
Regionalization of hydrological model parameters using gradient boosting machine
The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25∘; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.
Journal Article
Mapping Large‐Scale Deep Soil Moisture Variations Using Ambient Seismic Noise
Soil moisture is an essential ecosystem resource and a major control of the Earth's hydrological cycle and energy balance, closely interacting with the climate system. However, investigating deep soil moisture dynamics at large scales presents significant challenges due to the sparse distribution and limited spatial representativeness of in situ monitoring networks, while various remote sensing methods mainly address surface soil moisture within the top few centimeters. This study illustrates how seismic waves can effectively detect variations in deep soil moisture. We examine continuous seismic data from 791 stations across South‐Central Europe for the period 2016–2020. Our findings confirm a strong correlation between variations in seismic velocity and deep soil moisture content. Notably, the seismic observations pinpoint areas impacted by severe soil moisture deficits related to the 2016–2017 European drought event. The seismic method presented in this study offers new opportunities in addressing the observational gap of this critical environmental parameter.
Journal Article
Agroforestry delivers a win-win solution for ecosystem services in sub-Saharan Africa. A meta-analysis
Agricultural landscapes are increasingly being managed with the aim of enhancing the provisioning of multiple ecosystem services and sustainability of production systems. However, agricultural management that maximizes provisioning ecosystem services can often reduce both regulating and maintenance services. We hypothesized that agroforestry reduces trade-offs between provisioning and regulating/maintenance services. We conducted a quantitative synthesis of studies carried out in sub-Saharan Africa focusing on crop yield (as an indicator of provisioning services), soil fertility, erosion control, and water regulation (as indicators of regulating/maintenance services). A total of 1106 observations were extracted from 126 peer-reviewed publications that fulfilled the selection criteria for meta-analysis of studies comparing agroforestry and non-agroforestry practices (hereafter control) in sub-Saharan Africa. Across ecological conditions, agroforestry significantly increased crop yield, total soil nitrogen, soil organic carbon, and available phosphorus compared to the control. Agroforestry practices also reduced runoff and soil loss and improved infiltration rates and soil moisture content. No significant differences were detected between the different ecological conditions, management regimes, and types of woody perennials for any of the ecosystem services. Main trade-offs included low available phosphorus and low soil moisture against higher crop yield. This is the first meta-analysis that shows that, on average, agroforestry systems in sub-Saharan Africa increase crop yield while maintaining delivery of regulating/maintenance ecosystem services. We also demonstrate how woody perennials have been managed in agricultural landscapes to provide multiple ecosystem services without sacrificing crop productivity. This is important in rural livelihoods where the range of ecosystem services conveys benefits in terms of food security and resilience to environmental shocks.
Journal Article